我可以从带正方形的二进制图像中获取角度

Question

我想从Python的二进制图像中获取角度，但我不知道如何将此CPP转换为Python。

如果有其他方法可以从此“检查的图像”中检测角度，例如使用BLOB检测，请告诉我。

示例图像：

检查的图像

示例CPP代码：我

cv::Mat input = cv::imread("../inputData/rectangles.png");

cv::Mat gray;
cv::cvtColor(input,gray,CV_BGR2GRAY);

// since your image has compression artifacts, we have to threshold the image
int threshold = 200;
cv::Mat mask = gray > threshold;

cv::imshow("mask", mask);

// extract contours
std::vector<std::vector<cv::Point> > contours;
cv::findContours(mask, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);

for(int i=0; i<contours.size(); ++i)
{
    // fit bounding rectangle around contour
    cv::RotatedRect rotatedRect = cv::minAreaRect(contours[i]);

    // read points and angle
    cv::Point2f rect_points[4]; 
    rotatedRect.points( rect_points );

    float  angle = rotatedRect.angle; // angle

    // read center of rotated rect
    cv::Point2f center = rotatedRect.center; // center

    // draw rotated rect
    for(unsigned int j=0; j<4; ++j)
        cv::line(input, rect_points[j], rect_points[(j+1)%4], cv::Scalar(0,255,0));

    // draw center and print text
    std::stringstream ss;   ss << angle; // convert float to string
    cv::circle(input, center, 5, cv::Scalar(0,255,0)); // draw center
    cv::putText(input, ss.str(), center + cv::Point2f(-25,25), 
    cv::FONT_HERSHEY_COMPLEX_SMALL,1, cv::Scalar(255,0,255)); // print angle
}

的代码：我的代码：我的代码：

contours_yellow, hierarchy = cv2.findContours(mask_yellow, cv2.RETR_TREE, 
cv2.CHAIN_APPROX_SIMPLE)

yellow_contours = cv2.drawContours(mask_yellow, contours_yellow, -1, (0,255,0), 3)

angle = 0

for contour in yellow_contours :
{
    #fit bounding rectangle around contour
    RotatedRect = minAreaRect(contours_yellow[value])

    #read point and angle
    point2f rect_points[4]
    rotatedRect.points(rect_points)

    angle = rotatedRect.angle; # angle


}

    print (angle)

Answer 1

函数cv2.minarearect（）返回3个元素的元组：前两个是旋转矩形的坐标和旋转角度。我们需要为每个轮廓打印此最后一个元素。

在python中，要在list，array或tuple中获取最后一个元素，您可以做myList [-1]。

img = cv2.imread('white_boxes.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# threshold the image
th = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)[1]

# find contours 
contours, _ = cv2.findContours(th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

for c in contours:
  rect = cv2.minAreaRect(c)
  print('Angle:', rect[-1])